Step-by-step analysis: Gubin, Matthew M et al. “High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy.” Cell 175.4 (2018): 1014-1030.e19.
# In case multest and metap are not installed:
# install.packages('BiocManager')
# BiocManager::install('multtest')
# install.packages('metap')
library(plyr)
library(Matrix)
library(Seurat)
library(ggplot2)
library(cowplot)
library(metap)
library(multtest)
sobj<-readRDS(file="SingleCellCourseSeuratObject_Wk2S1.rds")
To identify canonical cell type marker genes that are conserved across conditions, we provide the FindConservedMarkers function. This function performs differential gene expression testing for each dataset/group and combines the p-values using meta-analysis methods from the MetaDE R package. For example, we can calculated the genes that are conserved markers irrespective of treatment condition in cluster XXX (XXX cells).
#ident.1 = cluster number. Here we are finding the markers for cluster 1
conservedmarkersAll<-FindConservedMarkers(sobj, ident.1 = 1, grouping.var = "treatment", verbose =FALSE)
conservedmarkersAll$clusters <- 1
#Creating a loop here that goes through the rest of the clusters, takes ~10 minutes to run. Results provided in class.
for (i in 2:12){
conservedmarkers<-FindConservedMarkers(sobj, ident.1 = i, grouping.var = "treatment", verbose =FALSE)
conservedmarkers$clusters <- i
conservedmarkersAll <- rbind(conservedmarkersAll,conservedmarkers)
}
#save results as a CSV file
write.csv(conservedmarkersAll, file="conservedmarkersAll.csv")
To retrieve a list of 40 markers for a given cluster, say cluster 3:
head(rownames(conservedmarkersAll[conservedmarkersAll$clusters==3,]), n=40)
## [1] "FTH11" "SLPI2" "ARG12" "CD141" "CSTB1" "CEBPB1"
## [7] "FCGR2B2" "INHBA" "FTL12" "GRINA" "LY6I2" "PRDX51"
## [13] "SLC7A112" "LGALS31" "F101" "MMP12" "CLEC4D1" "FCGR32"
## [19] "CTSL2" "ASS12" "SOD21" "PLIN21" "CTSS2" "RPL13A2"
## [25] "CXCL162" "CLEC4N1" "HILPDA" "ESD1" "SGK1" "CAPG1"
## [31] "MGST11" "IL1RN1" "PRDX61" "FCER1G2" "CYBB1" "H2.AA2"
## [37] "MIF" "BST11" "RPS111" "BNIP31"
DimPlot(sobj, reduction = "umap", pt.size = 0.1,label=TRUE) + ggtitle(label = "UMAP")
FeaturePlot(sobj, features = c("ADGRE1","CD3D","CD4","CD8B1","FOXP3","ITGAE","NCR1","S100A9","SIGLECH"), reduction = "umap", pt.size = 0.1)
Using the markers from above, we are able to identify the cell lineage of two large clusters.
sobj@meta.data$cell_lineage<- ifelse(sobj@meta.data$seurat_clusters == 12, "Doublets",
ifelse(sobj@meta.data$seurat_cluster %in%c(2,4,5,6,8),"Lymphoid","Myeloid"))
DimPlot(sobj,group.by="cell_lineage")
Macropahge and Monocyte Genes identified in Gubin, Matthew M et al
Markers: CD11c (ITGAX), CD64 (FCGR1), CD11B (ITGAM), CD14
FeaturePlot(sobj, features = c("ITGAX","FCGR1","ITGAM","CD14"), reduction = "umap", pt.size = 0.1)
Markers: CD273 (PDCD1LG2), CD192 (CCR2), CD115 (CSF1R), CD14
FeaturePlot(sobj, features=c("PDCD1LG2","CCR2","CSF1R","CD14"))
Markers: CD64 (FCGR1), CD206 (MRC1), CD14, FCER1G
FeaturePlot(sobj, features=c("FCGR1","MRC1","CD14","FCER1G"))
Markers: CD16 (FCGR3), CD32 (FCGR2B), NOS2, CD86
FeaturePlot(sobj, features=c("FCGR3","FCGR2B","NOS2","CD86"))
Markers: CD115 (CSF1R), CD44, CD45 (SPN), CXCR1, CD31 (PECAM1), CCR2
FeaturePlot(sobj, features=c("CSF1R","CD44","SPN","CX3CR1","PECAM1","CCR2"))
Markers: CD197 (CCR7), CD24A, CD205 (LY75), CD11C (ITGAX)
FeaturePlot(sobj,features=c("CCR7","CD24A","LY75","ITGAX","FLT3","CLEC9A"))
FeaturePlot(sobj, features = c("S100A9","CD24A","TNF","CD274"), reduction = "umap", pt.size = 0.1)
FeaturePlot(sobj, features = c("PTPRC","BST2","SIGLECH"), reduction = "umap", pt.size = 0.1)
sobj<- RenameIdents(sobj, `0` = "TAM", `1` = "M2 Macro",
`3` = "M1 Macro", `11` = "Monocytes", `7` = "DC", `9` = "Neurtophils", `10` = "pDC")
DimPlot(sobj, reduction = "umap", pt.size = 0.1,label=TRUE) + ggtitle(label = "UMAP")
Markers: CD3e - T cells
FeaturePlot(sobj, features=c("CD3E"))
Markers: CD4 T cells
FeaturePlot(sobj, features=c("CD4"))
Markers: FOXP3, CD4
FeaturePlot(sobj, features=c('FOXP3','CD4'))
Markers: CD3E, CD8A, CD8B1
FeaturePlot(sobj, features=c("CD3E", "CD8A","CD8B1"))
Markers: CD7,NCR1
FeaturePlot(sobj, features=c("CD7","NCR1"))
Markers: CD4, MKI67
FeaturePlot(sobj, features=c("CD4","MKI67"))
sobj<- RenameIdents(sobj, `0` = "TAM", `1` = "M2 Macro",
`3` = "M1 Macro", `11` = "Monocytes", `7` = "DC", `9` = "Neurtophils", `10` = "pDC",
`12`="Doublets", `6`="Mki67hi CD4 T Cells", `4`= "NK Cells", `5`="CD8 T cells",`8`="Tregs",
`2`="CD4 T cells")
DimPlot(sobj, reduction = "umap", pt.size = 0.1,label=TRUE) + ggtitle(label = "UMAP")
saveRDS(sobj,file="SingleCellCourseSeuratObject_Wk2S2.rds")